This application relates to techniques for modeling aggregation records, such as aggregation records associated with online analytical processing (OLAP).
There are many common ways for web services to model entities associated with online interactions and ecommerce. For example, there are many ways for web services to model a webpage, a blog post, an online audience member, an ad impression, and an invoice. Usually it is convenient to assign unique identifiers to entities, such as by assigning ID numbers incrementally. Systems can then use the unique identifiers to retrieve data from a database, memory, and/or an application programming interface (API), for example.
In the case of aggregate data, assigning unique identifiers can be difficult and using such identifiers may not be readily available. Often, data sources of aggregate data provide data without uniquely identifiable records. For example, online analytical processing (OLAP) data sources often do not use unique identifiers for their records. For instance, in a plurality of records of a collection of facts from an OLAP data source, each record may not include a unique identifier such as a serial number. Also, for instance, in a fact table of OLAP data, each row of facts in the table may not be unique or may not include a serial number or some other form of unique indicator for a row. In such systems, it can be difficult to retrieve individual records. Also, creating an entity map and caching and reusing such records can be complex. These technical problems can also make record sharing amongst various types of requesters (such as Javascript or PHP objects) problematic. This is especially the case when such sharing relies on cached records.
There is, therefore, a set of engineering problems that can be solved in generating and providing unique identifiers for records of aggregate data. Also, there is a set of technical problems to be solved in creating techniques to generate and provide unique identifiers for records of aggregate data. Resolution of such engineering problems is pertinent considering the vast benefits of using aggregate data (such as aggregate data from OLAP sources).
Additionally, aggregate data is especially useful for analytics in the competitive landscape of online advertising. The resolution of at least the aforementioned technical issues can benefit advertisers in providing more effective and efficient use of ad impressions and targeted advertising. For instance, such solutions may result in a greater number of user interactions with online ads. Also, these technical solutions can benefit online ad network providers in servicing advertisers and content providers providing ad spots for the ads. The novel technologies described herein also set out to solve a set of technical problems caused by the scope in analyzing data from online advertising sources. Today, there is room for improvement for resolving the aforementioned problems in uniquely identifying records of aggregate data and then using such records, efficiently and effectively, to improve analytical tasks. This is especially the case in online advertising.